
import io
import os
import pywt
import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.signal as sig
from scipy import signal
from pywt import wavedec
from scipy.io import loadmat
from scipy.stats import entropy
from scipy.signal import filtfilt
from scipy.signal import butter, lfilter, welch
from sklearn import datasets, linear_model, metrics


# Mengakses seluruh csv file pada drive
raw_data = pd.read_csv('data sama.csv', header = None)

# Membuat dataframe
df_raw = pd.DataFrame(raw_data)
df_raw

C:\Users\CLARA\AppData\Local\Temp\ipykernel_2796\2629672973.py:2: DtypeWarning: Columns (46,48,139,146,148,154,155,165,178,185,205,207,216,219,221,226,227,228,245,257,275,278,279) have mixed types. Specify dtype option on import or set low_memory=False.
  raw_data = pd.read_csv('data sama.csv', header = None)


# Set the sampling frequency in Hz
fs = 128

# Define the time ranges to crop
start_time = 20
end_time = 30
start_time1 = 40
end_time1 = 50

# Convert the time ranges to index ranges
start_idx = int(start_time * fs)
end_idx = int(end_time * fs)
start_idx1 = int(start_time1 * fs)
end_idx1 = int(end_time1 * fs)

# Crop the data to the first time range
hold_hand = df_raw.loc[start_idx:end_idx]

# Crop the data to the second time range
open_hand = df_raw.loc[start_idx1:end_idx1]

hold_hand
hold_hand.to_csv('Hold Hand Raw Data.csv')


open_hand
open_hand.to_csv('Open Hand Raw Data.csv')


hold_hand


open_hand


hold_f3 = pd.read_csv('Data Hold Hand F3.csv', header = None)
hold_f3


# Original Hold Hand F3
for column in hold_f3.columns:  # Loop melalui kolom-kolom DataFrame
  plt.figure()
  plt.title(f"Column-{column}")  # Display the custom title
  plt.plot(hold_f3[column])
  plt.show()


open_f3 = pd.read_csv('Data Open Hand F3.csv', header = None)
open_f3


# Original Open F3
for column in open_f3.columns:  # Loop melalui kolom-kolom DataFrame
  plt.figure()
  plt.title(f"Column-{column}")  # Display the custom title
  plt.plot(open_f3[column])
  plt.show()


# Filtering
fs = 128
highcut = 30
lowcut = 0.5

def butter_bandpass(lowcut, highcut, fs, order=4):
    nyq = 0.5 * fs
    low = lowcut / nyq
    high = highcut / nyq
    filter_coeffs = signal.firwin(order, [low, high], pass_zero=False)
    b = filter_coeffs
    a = 1
    return b, a

def apply_bandpass_filter(hold_f3, lowcut, highcut, fs):
    b, a = butter_bandpass(lowcut, highcut, fs)
    filtered_data = np.zeros_like(hold_f3)
    for i, column in enumerate(hold_f3.columns):
        data = hold_f3[column].values
        filtered_data[:, i] = signal.filtfilt(b, a, data)
    return pd.DataFrame(filtered_data, columns=hold_f3.columns)

# Apply the bandpass filter to all columns in the hold_f3 dataframe
hold_hand_filtered = apply_bandpass_filter(hold_f3, lowcut, highcut, fs)
filtered_hold_data = pd.DataFrame(hold_hand_filtered)
filtered_hold_data


# Filtering Hold F3
for column in hold_hand_filtered.columns:  # Loop melalui kolom-kolom DataFrame
  plt.figure()
  plt.title(f"Column-{column}")  # Display the custom title
  plt.plot(hold_hand_filtered[column])
  plt.show()


# Filtering Open F3
def apply_bandpass_filter(open_f3, lowcut, highcut, fs):
    b, a = butter_bandpass(lowcut, highcut, fs)
    filtered_data = np.zeros_like(open_f3)
    for i, column in enumerate(open_f3.columns):
        data = open_f3[column].values
        filtered_data[:, i] = signal.filtfilt(b, a, data)
    return pd.DataFrame(filtered_data, columns=open_f3.columns)

# Apply the bandpass filter to all columns in the open_f3 dataframe
open_hand_filtered = apply_bandpass_filter(open_f3, lowcut, highcut, fs)
filtered_open_data = pd.DataFrame(open_hand_filtered)
filtered_open_data


# Filtering Open F3
for column in open_hand_filtered.columns:  # Loop melalui kolom-kolom DataFrame
  plt.figure()
  plt.title(f"Column-{column}")  # Display the custom title
  plt.plot(open_hand_filtered[column])
  plt.show()


# Compute FFT for each column in hold_f3_filtered dataframe
fft_data = np.fft.fft(hold_hand_filtered.values, axis=0)
fft_freq = np.fft.fftfreq(hold_hand_filtered.shape[0], 1/fs)
fft_hold_df = pd.DataFrame(columns=hold_hand_filtered.columns, index=fft_freq[:hold_hand_filtered.shape[0]//2])

# Plot FFT for each column
for i, column in enumerate(hold_hand_filtered.columns):
  fig, ax=plt.subplots(figsize=(8,6))
  fft_hold_df[column] = np.abs(fft_data[:hold_hand_filtered.shape[0]//2, i])
  ax.plot(fft_hold_df[column])
  ax.set_xlabel('Frequency (Hz)')
  ax.set_ylabel('Magnitude')
  ax.set_xlim([0,65])
  ax.set_ylim([0,50000])
  plt.show()

fft_hold_df
fft_hold_indexing = fft_hold_df.reset_index(drop = True)
fft_hold_indexing


# Compute FFT for each column in open_f3_filtered dataframe
fft_data = np.fft.fft(open_hand_filtered.values, axis=0)
fft_freq = np.fft.fftfreq(open_hand_filtered.shape[0], 1/fs)
fft_open_df = pd.DataFrame(columns=open_hand_filtered.columns, index=fft_freq[:open_hand_filtered.shape[0]//2])

# Plot FFT for each column
for i, column in enumerate(open_hand_filtered.columns):
  fig, ax=plt.subplots(figsize=(8,6))
  fft_open_df[column] = np.abs(fft_data[:open_hand_filtered.shape[0]//2, i])
  ax.plot(fft_open_df[column])
  ax.set_xlabel('Frequency (Hz)')
  ax.set_ylabel('Magnitude')
  ax.set_xlim([0,65])
  ax.set_ylim([0,50000])
  plt.show()

fft_open_df
fft_open_indexing = fft_open_df.reset_index(drop = True)
fft_open_indexing


# Power Spectral Density Hold
def extract_band_power(fft_hold_indexing, fs, band, level):
    # Create an empty dictionary to hold the band powers for each column
    band_powers = {}
    
    # Loop over all columns in the DataFrame
    for column in fft_hold_indexing.columns:
        # Apply wavelet decomposition with specified level
        coeffs = wavedec(fft_hold_indexing[column], 'db4', level=level)

        # Get the approximate coefficients of the specified level
        cA = coeffs[level]

        # Compute power spectral density (PSD) using Welch's method
        f, Pxx = welch(cA, fs, nperseg=min(256, len(cA)))

        # Define frequency bands
        freq_bands = {'delta': [0.5, 4],
                      'theta': [4, 8],
                      'alpha': [8, 13],
                      'beta': [13, 30]}

        # Define frequency indices
        freq_ix = {'delta': np.logical_and(f >= freq_bands['delta'][0], f < freq_bands['delta'][1]),
                   'theta': np.logical_and(f >= freq_bands['theta'][0], f < freq_bands['theta'][1]),
                   'alpha': np.logical_and(f >= freq_bands['alpha'][0], f < freq_bands['alpha'][1]),
                   'beta': np.logical_and(f >= freq_bands['beta'][0], f < freq_bands['beta'][1])}

        # Calculate power in the specified band
        mask = freq_ix[band]
        band_power = np.trapz(y=Pxx[mask], x=f[mask])

        # Add the band power to the dictionary for this column
        band_powers[column] = band_power
    
    # Create a DataFrame from the band powers dictionary and return it
    return pd.DataFrame.from_dict(band_powers, orient='index', columns=[band])

# Define the sampling frequency and wavelet decomposition level
fs = 128
level = 4

# Define the input DataFrame with 20 columns of hold hand F3 data
# Replace this with your own DataFrame
fft_hold_indexing = pd.DataFrame(np.random.randn(1000, 20))

# Loop over all frequency bands and compute the band powers for each column
band_power_hold = pd.DataFrame()
for band in ['delta', 'theta', 'alpha', 'beta']:
    band_power = extract_band_power(fft_hold_indexing, fs, band, level)
    band_power_hold = pd.concat([band_power_hold, band_power], axis=1)

band_power_hold


# Power Spectral Density Open
def extract_band_power(fft_open_indexing, fs, band, level):
    # Create an empty dictionary to open the band powers for each column
    band_powers = {}
    
    # Loop over all columns in the DataFrame
    for column in fft_open_indexing.columns:
        # Apply wavelet decomposition with specified level
        coeffs = wavedec(fft_open_indexing[column], 'db4', level=level)

        # Get the approximate coefficients of the specified level
        cA = coeffs[level]

        # Compute power spectral density (PSD) using Welch's method
        f, Pxx = welch(cA, fs, nperseg=min(256, len(cA)))

        # Define frequency bands
        freq_bands = {'delta': [0.5, 4],
                      'theta': [4, 8],
                      'alpha': [8, 13],
                      'beta': [13, 30]}

        # Define frequency indices
        freq_ix = {'delta': np.logical_and(f >= freq_bands['delta'][0], f < freq_bands['delta'][1]),
                   'theta': np.logical_and(f >= freq_bands['theta'][0], f < freq_bands['theta'][1]),
                   'alpha': np.logical_and(f >= freq_bands['alpha'][0], f < freq_bands['alpha'][1]),
                   'beta': np.logical_and(f >= freq_bands['beta'][0], f < freq_bands['beta'][1])}

        # Calculate power in the specified band
        mask = freq_ix[band]
        band_power = np.trapz(y=Pxx[mask], x=f[mask])

        # Add the band power to the dictionary for this column
        band_powers[column] = band_power
    
    # Create a DataFrame from the band powers dictionary and return it
    return pd.DataFrame.from_dict(band_powers, orient='index', columns=[band])

# Define the sampling frequency and wavelet decomposition level
fs = 128
level = 4

# Define the input DataFrame with 20 columns of open hand F3 data
# Replace this with your own DataFrame
fft_open_indexing = pd.DataFrame(np.random.randn(1000, 20))

# Loop over all frequency bands and compute the band powers for each column
band_power_open = pd.DataFrame()
for band in ['delta','theta','alpha','beta']:
    band_power = extract_band_power(fft_open_indexing, fs, band, level)
    band_power_open= pd.concat([band_power_open, band_power], axis=1)

band_power_open


from scipy.stats import entropy
entropy_hold = np.apply_along_axis(entropy,1,abs(band_power_hold))
entropy_hold_df = pd.DataFrame(entropy_hold, columns=['entropy'])

entropy_hold_df


from scipy.stats import entropy
entropy_open = np.apply_along_axis(entropy,1,abs(band_power_open))
entropy_open_df = pd.DataFrame(entropy_open, columns=['entropy'])

entropy_open_df


df_hold = pd.concat([band_power_hold, entropy_hold_df], axis=1)
df_hold


df_open = pd.concat([band_power_open,entropy_open_df], axis=1)
df_open


# Mencari mean hold
mean_hold = df_hold.mean(axis=1)
df_mean_hold = pd.DataFrame(mean_hold, columns=['mean'])
df_mean_hold


# Mencari mean open
mean_open = df_open.mean(axis=1)
df_mean_open = pd.DataFrame(mean_open, columns=['mean'])
df_mean_open


# Mencari variance hold
var_hold = df_hold.var(axis=1)
df_var_hold = pd.DataFrame(var_hold, columns=['var'])
df_var_hold


#Mencari variance open
var_open = df_open.var(axis=1)
df_var_open = pd.DataFrame(var_open, columns=['var'])
df_var_open


# Mencari std hold
std_hold = df_hold.std(axis=1)
df_std_hold = pd.DataFrame(std_hold, columns=['std'])
df_std_hold


#Mencari std open
std_open = df_open.std(axis=1)
df_std_open = pd.DataFrame(std_open, columns=['std'])
df_std_open


# Mencari min hold
min_hold = df_hold.min(axis=1)
df_min_hold = pd.DataFrame(min_hold, columns=['min'])
df_min_hold


# Mencari min open
min_open = df_open.min(axis=1)
df_min_open = pd.DataFrame(min_open, columns=['min'])
df_min_open


#Mencari max hold
max_hold = df_hold.max(axis=1)
df_max_hold = pd.DataFrame(max_hold, columns=['max'])
df_max_hold


#Mencari max open
max_open = df_open.max(axis=1)
df_max_open = pd.DataFrame(max_open, columns=['max'])
df_max_open


#Membuat dataframe baru dari feature yang baru
df_feat_hold = pd.concat([df_mean_hold, df_std_hold, df_var_hold, df_min_hold, df_max_hold], axis=1)
df_feat_hold


#Membuat dataframe baru dari feature yang baru
df_feat_open = pd.concat([df_mean_open, df_std_open, df_var_open, df_min_open, df_max_open], axis=1)
df_feat_open


df_combi_hold = pd.concat([df_hold, df_feat_hold], axis=1)
df_combi_hold


from sklearn.preprocessing import MinMaxScaler

# Create an instance of the MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))

# Normalize the features in the DataFrame
normalized_df_combi_hold = pd.DataFrame(scaler.fit_transform(df_combi_hold), columns=df_combi_hold.columns)
normalized_df_combi_hold


df_combi_open = pd.concat([df_open, df_feat_open], axis=1)
df_combi_open


from sklearn.preprocessing import MinMaxScaler

# Create an instance of the MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))

# Normalize the features in the DataFrame
normalized_df_combi_open = pd.DataFrame(scaler.fit_transform(df_combi_open), columns=df_combi_open.columns)
normalized_df_combi_open


# Targeting
df_hold_target=pd.DataFrame(normalized_df_combi_hold)
df_hold_target['target']= 0
df_hold_target


# Targeting
df_open_target=pd.DataFrame(normalized_df_combi_open)
df_open_target['target']= 1
df_open_target


df_combi = pd.concat([normalized_df_combi_hold, normalized_df_combi_open])
df_combi


import seaborn as sns

# Compute the correlation matrix
correlation_matrix = df_combi.corr()

# Create a heatmap with labels
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')

# Add labels to the x and y axis ticks
plt.xticks(range(len(correlation_matrix.columns)), correlation_matrix.columns)
plt.yticks(range(len(correlation_matrix.columns)), correlation_matrix.columns)

# Display the plot
plt.show()


#Splitting Data into features and target
x=df_combi.loc[:,'delta':'max'].to_numpy() #features
y=df_combi.loc[:,'target'].to_numpy() #target


import collections
counter = collections.Counter(y)
for label, _ in counter.items():
    row_ix = np.where(y == label)[0]
    plt.scatter(x[row_ix, 0], x[row_ix, 1], label=label)
plt.legend()
plt.show()


from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)


# Training Model - Melakukan training model dengan menggunakan k = 1
from sklearn.neighbors import KNeighborsClassifier
k1 = 1
model_knn_1 = KNeighborsClassifier(n_neighbors = k1)
model_knn_1.fit(x_train, y_train)

KNeighborsClassifier(n_neighbors=1)


# Predicting
y_pred_1 = model_knn_1.predict(x_test)
print(y_pred_1)

[1 1 1 1 1 0 0 1 0 1 1 1]

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Perhitungan Akurasi k = 1
from sklearn import metrics
print("Train set Accuracy : ", metrics.accuracy_score(y_train, model_knn_1.predict(x_train)))
print("Test set Accuracy : ", metrics.accuracy_score(y_test, y_pred_1))

Train set Accuracy :  1.0
Test set Accuracy :  0.6666666666666666

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Training Model - Melakukan training model dengan menggunakan k = 2
from sklearn.neighbors import KNeighborsClassifier
k2 = 2
model_knn_2 = KNeighborsClassifier(n_neighbors = k2)
model_knn_2.fit(x_train, y_train)

KNeighborsClassifier(n_neighbors=2)


# Predicting
y_pred_2 = model_knn_2.predict(x_test)
print(y_pred_2)

[1 0 1 0 1 0 0 0 0 1 0 1]

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Perhitungan Akurasi K = 2
from sklearn import metrics
print("Train set Accuracy : ", metrics.accuracy_score(y_train, model_knn_2.predict(x_train)))
print("Test set Accuracy : ", metrics.accuracy_score(y_test, y_pred_2))

Train set Accuracy :  0.7857142857142857
Test set Accuracy :  0.8333333333333334

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Training Model - Melakukan training model dengan menggunakan k = 3
from sklearn.neighbors import KNeighborsClassifier
k3 = 3
model_knn_3 = KNeighborsClassifier(n_neighbors = k3)
model_knn_3.fit(x_train, y_train)

KNeighborsClassifier(n_neighbors=3)


# Predicting
y_pred_3 = model_knn_3.predict(x_test)
print(y_pred_3)

[1 1 1 1 1 0 0 0 0 1 1 1]

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Perhitungan Akurasi K = 3
from sklearn import metrics
print("Train set Accuracy : ", metrics.accuracy_score(y_train, model_knn_3.predict(x_train)))
print("Test set Accuracy : ", metrics.accuracy_score(y_test, y_pred_3))

Train set Accuracy :  0.7857142857142857
Test set Accuracy :  0.75

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Training Model - Melakukan training model dengan menggunakan k = 4

from sklearn.neighbors import KNeighborsClassifier

k4 = 4

model_knn_4 = KNeighborsClassifier(n_neighbors = k4)
model_knn_4.fit(x_train, y_train)

KNeighborsClassifier(n_neighbors=4)


# Predicting

y_pred_4 = model_knn_4.predict(x_test)
print(y_pred_4)

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)

[0 1 1 1 1 0 0 0 0 1 0 1]


# Perhitungan Akurasi K = 4

from sklearn import metrics

print("Train set Accuracy : ", metrics.accuracy_score(y_train, model_knn_4.predict(x_train)))
print("Test set Accuracy : ", metrics.accuracy_score(y_test, y_pred_4))

Train set Accuracy :  0.75
Test set Accuracy :  0.75

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Training Model - Melakukan training model dengan menggunakan k = 5

from sklearn.neighbors import KNeighborsClassifier

k5 = 5

model_knn_5 = KNeighborsClassifier(n_neighbors = k5)
model_knn_5.fit(x_train, y_train)

KNeighborsClassifier()


# Predicting

y_pred_5 = model_knn_5.predict(x_test)
print(y_pred_5)

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)

[1 1 1 1 1 0 0 0 0 1 0 1]


# Perhitungan Akurasi K = 5

from sklearn import metrics

print("Train set Accuracy : ", metrics.accuracy_score(y_train, model_knn_5.predict(x_train)))
print("Test set Accuracy : ", metrics.accuracy_score(y_test, y_pred_5))

Train set Accuracy :  0.7857142857142857
Test set Accuracy :  0.8333333333333334

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Training Model - Melakukan training model dengan menggunakan k = 6

from sklearn.neighbors import KNeighborsClassifier

k6 = 6

model_knn_6 = KNeighborsClassifier(n_neighbors = k6)
model_knn_6.fit(x_train, y_train)

KNeighborsClassifier(n_neighbors=6)


# Predicting

y_pred_6 = model_knn_6.predict(x_test)
print(y_pred_6)

[0 1 1 1 1 0 0 0 0 1 0 1]

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Perhitungan Akurasi K = 6

from sklearn import metrics

print("Train set Accuracy : ", metrics.accuracy_score(y_train, model_knn_6.predict(x_train)))
print("Test set Accuracy : ", metrics.accuracy_score(y_test, y_pred_6))

Train set Accuracy :  0.8214285714285714
Test set Accuracy :  0.75

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Training Model - Melakukan training model dengan menggunakan k = 7

from sklearn.neighbors import KNeighborsClassifier

k7 = 7

model_knn_7 = KNeighborsClassifier(n_neighbors = k7)
model_knn_7.fit(x_train, y_train)

KNeighborsClassifier(n_neighbors=7)


# Predicting

y_pred_7 = model_knn_7.predict(x_test)
print(y_pred_7)

[1 1 1 1 1 0 0 0 0 1 0 1]

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Perhitungan Akurasi K = 7

from sklearn import metrics

print("Train set Accuracy : ", metrics.accuracy_score(y_train, model_knn_7.predict(x_train)))
print("Test set Accuracy : ", metrics.accuracy_score(y_test, y_pred_7))

Train set Accuracy :  0.7857142857142857
Test set Accuracy :  0.8333333333333334

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Training Model - Melakukan training model dengan menggunakan k = 8

from sklearn.neighbors import KNeighborsClassifier

k8 = 8

model_knn_8 = KNeighborsClassifier(n_neighbors = k8)
model_knn_8.fit(x_train, y_train)

KNeighborsClassifier(n_neighbors=8)


# Predicting

y_pred_8 = model_knn_8.predict(x_test)
print(y_pred_8)

[0 1 1 1 1 0 0 0 0 1 0 1]

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Perhitungan Akurasi K = 8

from sklearn import metrics

print("Train set Accuracy : ", metrics.accuracy_score(y_train, model_knn_8.predict(x_train)))
print("Test set Accuracy : ", metrics.accuracy_score(y_test, y_pred_8))

Train set Accuracy :  0.7857142857142857
Test set Accuracy :  0.75

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Training Model - Melakukan training model dengan menggunakan k = 9

from sklearn.neighbors import KNeighborsClassifier

k9 = 9

model_knn_9 = KNeighborsClassifier(n_neighbors = k9)
model_knn_9.fit(x_train, y_train)

KNeighborsClassifier(n_neighbors=9)


# Predicting

y_pred_9 = model_knn_9.predict(x_test)
print(y_pred_9)

[1 1 1 1 1 0 0 0 0 1 0 1]

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Perhitungan Akurasi K = 9

from sklearn import metrics

print("Train set Accuracy : ", metrics.accuracy_score(y_train, model_knn_9.predict(x_train)))
print("Test set Accuracy : ", metrics.accuracy_score(y_test, y_pred_9))

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)

Train set Accuracy :  0.7857142857142857
Test set Accuracy :  0.8333333333333334


# Training Model - Melakukan training model dengan menggunakan k = 10

from sklearn.neighbors import KNeighborsClassifier

k10 = 10

model_knn_10 = KNeighborsClassifier(n_neighbors = k10)
model_knn_10.fit(x_train, y_train)

KNeighborsClassifier(n_neighbors=10)


# Predicting

y_pred_10 = model_knn_10.predict(x_test)
print(y_pred_10)

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)

[1 1 1 1 1 0 0 0 0 1 0 1]


# Perhitungan Akurasi K = 10

from sklearn import metrics

print("Train set Accuracy : ", metrics.accuracy_score(y_train, model_knn_10.predict(x_train)))
print("Test set Accuracy : ", metrics.accuracy_score(y_test, y_pred_10))

Train set Accuracy :  0.8214285714285714
Test set Accuracy :  0.8333333333333334

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


model_eval = pd.DataFrame(columns=['Train Accuracy', 'Test Accuracy'])

# Train Set Accuracy
model_eval.loc['K = 1','Train Accuracy'] = metrics.accuracy_score(y_train, model_knn_1.predict(x_train))
model_eval.loc['K = 2','Train Accuracy'] = metrics.accuracy_score(y_train, model_knn_2.predict(x_train))
model_eval.loc['K = 3','Train Accuracy'] = metrics.accuracy_score(y_train, model_knn_3.predict(x_train))
model_eval.loc['K = 4','Train Accuracy'] = metrics.accuracy_score(y_train, model_knn_4.predict(x_train))
model_eval.loc['K = 5','Train Accuracy'] = metrics.accuracy_score(y_train, model_knn_5.predict(x_train))
model_eval.loc['K = 6','Train Accuracy'] = metrics.accuracy_score(y_train, model_knn_6.predict(x_train))
model_eval.loc['K = 7','Train Accuracy'] = metrics.accuracy_score(y_train, model_knn_7.predict(x_train))
model_eval.loc['K = 8','Train Accuracy'] = metrics.accuracy_score(y_train, model_knn_8.predict(x_train))
model_eval.loc['K = 9','Train Accuracy'] = metrics.accuracy_score(y_train, model_knn_9.predict(x_train))
model_eval.loc['K = 10','Train Accuracy'] = metrics.accuracy_score(y_train, model_knn_10.predict(x_train))
model_eval 

# Test Set Accuracy 
model_eval.loc['K = 1','Test Accuracy'] = metrics.accuracy_score(y_test, y_pred_1)
model_eval.loc['K = 2','Test Accuracy'] = metrics.accuracy_score(y_test, y_pred_2)
model_eval.loc['K = 3','Test Accuracy'] = metrics.accuracy_score(y_test, y_pred_3)
model_eval.loc['K = 4','Test Accuracy'] = metrics.accuracy_score(y_test, y_pred_4)
model_eval.loc['K = 5','Test Accuracy'] = metrics.accuracy_score(y_test, y_pred_5)
model_eval.loc['K = 6','Test Accuracy'] = metrics.accuracy_score(y_test, y_pred_6)
model_eval.loc['K = 7','Test Accuracy'] = metrics.accuracy_score(y_test, y_pred_7)
model_eval.loc['K = 8','Test Accuracy'] = metrics.accuracy_score(y_test, y_pred_8)
model_eval.loc['K = 9','Test Accuracy'] = metrics.accuracy_score(y_test, y_pred_9)
model_eval.loc['K = 10','Test Accuracy'] = metrics.accuracy_score(y_test, y_pred_10)
model_eval

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Retraining Menggunakan Nilai K dengan Akurasi Terbaik - K = 10

k = 10

model_knn = KNeighborsClassifier(n_neighbors = k)
model_knn.fit(x_train, y_train)

KNeighborsClassifier(n_neighbors=10)


# Predicting
y_pred = model_knn.predict(x_test)
print(y_pred)

[1 1 1 1 1 0 0 0 0 1 0 1]

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Perhitungan Akurasi k = 10

print("Train set Accuracy : ", metrics.accuracy_score(y_train, model_knn.predict(x_train)))
print("Test set Accuracy : ", metrics.accuracy_score(y_test, y_pred))
print()
print(f"Achieved accuracy of {metrics.accuracy_score(y_test, y_pred)*100}% on test data")

Train set Accuracy :  0.8214285714285714
Test set Accuracy :  0.8333333333333334

Achieved accuracy of 83.33333333333334% on test data

C:\Users\CLARA\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)


# Confusion Matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
import seaborn as sns
target_names = ['hold hand', 'open hand']
print(classification_report(y_test, y_pred, target_names=target_names))

f, ax = plt.subplots(figsize=(8,5))
cm = confusion_matrix(y_test, y_pred)
cm_labels = ['hold hand', 'open hand']
sns.heatmap(cm,cmap= "Blues", linecolor = 'black' , linewidth = 1 , annot = True, fmt='', 
            yticklabels = cm_labels,
            xticklabels = cm_labels, ax=ax)
plt.xlabel("Actual Values")
plt.ylabel("Predicted Values")
plt.show()

              precision    recall  f1-score   support

   hold hand       1.00      0.71      0.83         7
   open hand       0.71      1.00      0.83         5

    accuracy                           0.83        12
   macro avg       0.86      0.86      0.83        12
weighted avg       0.88      0.83      0.83        12

	0	1	2	3	4	5	6	7	8	9	...	270	271	272	273	274	275	276	277	278	279
0	4498.974121	3972.820313	4404.615234	4195.384277	4249.230469	4412.820313	4278.974121	4551.794922	3853.333252	5752.820313	...	4254.871582	4443.076660	4194.358887	4396.922852	3394.871826	3908.205078.1	4274.358887	4098.974121	4179.487305.1	4047.179443.2
1	4501.538574	3974.871582	4408.205078	4194.871582	4250.256348	4412.307617	4257.948730	4545.127930	3838.461426	5740.000000	...	4253.846191	4445.127930	4190.256348	4389.230469	3385.128174	3902.05127	4269.230469	4089.230713	4175.897461	4003.5896
2	4502.051270	3983.589600	4409.230469	4195.897461	4247.692383	4415.384277	4253.333008	4533.846191	3834.871582	5707.179199	...	4254.358887	4446.153809	4195.897461	4387.692383	3394.871826	3907.692139	4272.307617	4076.923096	4177.436035	3921.53833
3	4504.102539	3992.307617	4410.769043	4201.025391	4248.717773	4417.948730	4271.794922	4521.025391	3837.948730	5673.846191	...	4256.922852	4445.127930	4194.871582	4392.307617	3416.923096	3918.974365	4284.102539	4082.051270	4188.205078	3922.05127
4	4504.102539	3984.102539	4412.307617	4200.000000	4248.205078	4418.974121	4276.922852	4506.153809	3834.871582	5654.358887	...	4252.820313	4444.102539	4189.230469	4387.692383	3410.256348	3911.794678	4280.512695	4090.769043	4189.743652	3975.897217
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7163	4546.153809	3981.538330	4456.410156	4245.641113	4283.076660	4489.743652	4332.307617	4650.256348	3811.281982	4320.512695	...	4240.000000	4443.589844	4259.487305	4534.871582	3713.845947	4277.436035	4536.410156	3342.563965	4132.820313	643.589722
7164	4546.153809	3981.538330	4456.410156	4245.641113	4283.076660	4489.743652	4332.307617	4650.256348	3811.281982	4320.512695	...	4240.000000	4443.589844	4259.487305	4534.871582	3713.845947	4277.436035	4536.410156	3342.563965	4132.820313	643.589722
7165	4546.153809	3981.538330	4456.410156	4245.641113	4283.076660	4489.743652	4332.307617	4650.256348	3811.281982	4320.512695	...	4240.000000	4443.589844	4259.487305	4534.871582	3713.845947	4277.436035	4536.410156	3342.563965	4132.820313	643.589722
7166	4546.153809	3981.538330	4456.410156	4245.641113	4283.076660	4489.743652	4332.307617	4650.256348	3811.281982	4320.512695	...	4240.000000	4443.589844	4259.487305	4534.871582	3713.845947	4277.436035	4536.410156	3342.563965	4132.820313	643.589722
7167	4546.153809	3981.538330	4456.410156	4245.641113	4283.076660	4489.743652	4332.307617	4650.256348	3811.281982	4320.512695	...	4240.000000	4443.589844	4259.487305	4534.871582	3713.845947	4277.436035	4536.410156	3342.563965	4132.820313	643.589722

	0	1	2	3	4	5	6	7	8	9	...	270	271	272	273	274	275	276	277	278	279
2560	4517.435547	3978.974365	4173.846191	4195.897461	4254.871582	4436.410156	4157.948730	4554.871582	3529.230713	4273.333008	...	4307.692383	4489.230469	4227.692383	4258.974121	3180.000000	3660.512695	4055.384521	3695.897461	3986.666504	1776.410156
2561	4512.307617	3971.794678	4163.076660	4198.974121	4251.794922	4436.922852	4145.127930	4542.563965	3521.538330	4263.589844	...	4300.512695	4488.205078	4212.820313	4248.717773	3148.717773	3626.153809	4033.845947	3772.820313	3997.94873	2337.435791
2562	4512.307617	3976.410156	4157.948730	4198.974121	4251.794922	4437.435547	4153.333008	4540.512695	3512.307617	4258.461426	...	4301.025391	4489.230469	4220.512695	4253.846191	3134.871826	3607.179443	4024.102539	3846.666504	4027.692139	2803.076904
2563	4515.384277	3984.615234	4161.025391	4195.897461	4251.281738	4436.922852	4167.692383	4550.256348	3519.487061	4268.205078	...	4307.179199	4490.769043	4228.717773	4253.846191	3127.692139	3595.897461	4023.076904	3920.512695	4054.358887	3228.717773
2564	4514.871582	3968.205078	4161.025391	4194.871582	4248.717773	4438.974121	4150.769043	4546.153809	3526.666504	4268.717773	...	4308.205078	4490.769043	4228.717773	4243.589844	3113.333252	3581.53833	4014.871582	3946.666504	4055.384521	3388.205078
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3836	4506.666504	4009.230713	4415.384277	4208.717773	4264.615234	4317.948730	4260.512695	4583.076660	3811.281982	4314.871582	...	4204.102539	4438.974121	4243.589844	4425.127930	3594.871826	4117.436035	4540.000000	4018.974365	4366.666504	3556.410156
3837	4505.641113	4009.743408	4413.333008	4206.666504	4262.563965	4311.794922	4258.461426	4584.102539	3812.307617	4313.846191	...	4203.589844	4438.461426	4236.410156	4432.820313	3592.307617	4117.436035	4540.000000	4028.205078	4365.12793	3607.692139
3838	4506.153809	4008.717773	4410.769043	4201.025391	4261.538574	4310.256348	4262.563965	4589.230469	3818.461426	4315.384277	...	4201.538574	4435.897461	4234.871582	4441.025391	3591.281982	4113.333008	4537.435547	4025.128174	4363.07666	3626.666504
3839	4505.641113	4007.692139	4409.743652	4202.051270	4262.563965	4311.794922	4266.666504	4584.102539	3815.897461	4305.641113	...	4203.589844	4434.871582	4235.384277	4429.743652	3584.615234	4108.717773	4535.897461	4024.102539	4364.615234	3621.53833
3840	4505.641113	4010.769043	4412.307617	4207.692383	4264.102539	4313.846191	4280.512695	4586.666504	3813.333252	4310.256348	...	4203.589844	4436.922852	4237.948730	4422.051270	3581.538330	4106.153809	4531.794922	4027.179443	4365.641113	3621.025635

	0	1	2	3	4	5	6	7	8	9	...	270	271	272	273	274	275	276	277	278	279
5120	4528.205078	3990.256348	4276.410156	4200.512695	4262.051270	4445.127930	4126.153809	4460.000000	3488.205078	4230.256348	...	4265.127930	4457.435547	4211.281738	4398.461426	3554.358887	4067.692139	4460.000000	3993.845947	4296.410156	3170.769043
5121	4525.127930	3990.769043	4277.948730	4200.000000	4262.051270	4445.127930	4138.974121	4466.153809	3488.717773	4236.410156	...	4261.538574	4453.333008	4208.717773	4407.692383	3564.102539	4083.076904	4473.333008	3976.923096	4294.358887	3046.666504
5122	4524.102539	3990.769043	4277.436035	4202.051270	4262.051270	4444.102539	4144.615234	4463.589844	3488.717773	4231.281738	...	4261.025391	4453.333008	4205.127930	4410.256348	3549.230713	4080.512695	4468.717773	3956.923096	4286.153809	2944.615234
5123	4522.051270	3988.717773	4271.794922	4198.974121	4261.025391	4443.076660	4119.487305	4452.820313	3490.769043	4215.384277	...	4265.127930	4459.487305	4214.358887	4413.846191	3548.205078	4084.615234	4471.281738	3967.179443	4294.358887	2969.743408
5124	4525.127930	3990.769043	4272.307617	4198.974121	4260.512695	4444.615234	4105.641113	4450.769043	3492.820313	4216.410156	...	4265.127930	4462.051270	4224.615234	4415.897461	3551.794678	4083.076904	4471.794922	3976.923096	4294.871582	3023.5896
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6396	4502.563965	3972.307617	4327.692383	4216.922852	4278.461426	4475.897461	4286.666504	4619.487305	3809.743408	4366.153809	...	4244.615234	4438.461426	4262.051270	4478.461426	3696.923096	4230.769043	4601.025391	3945.640869	4408.717773	2916.923096
6397	4506.153809	3977.948730	4332.820313	4213.333008	4276.922852	4472.820313	4288.717773	4617.435547	3813.333252	4360.000000	...	4241.538574	4435.384277	4259.487305	4494.358887	3688.717773	4228.205078	4598.974121	3954.871582	4404.615234	3003.5896
6398	4503.589844	3977.948730	4329.743652	4209.743652	4271.281738	4469.743652	4281.025391	4615.384277	3810.256348	4356.922852	...	4245.641113	4437.948730	4256.922852	4492.307617	3682.563965	4217.94873	4596.922852	3983.076904	4411.281738	3177.94873
6399	4504.102539	3975.897217	4328.205078	4211.281738	4271.281738	4473.846191	4285.127930	4615.384277	3810.256348	4355.384277	...	4244.615234	4437.948730	4257.436035	4473.846191	3680.512695	4207.179199	4590.256348	4003.589600	4417.94873	3286.666504
6400	4505.127930	3969.230713	4327.692383	4211.794922	4269.230469	4470.256348	4284.615234	4605.641113	3814.358887	4341.538574	...	4240.512695	4434.358887	4255.384277	4468.205078	3675.384521	4200.0	4582.051270	4004.102539	4412.307617	3303.076904

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
0	4173.846191	4272.820313	4504.615234	4340.000000	4192.307617	4264.615234	4381.538574	4302.563965	4319.487305	4405.641113	4426.666504	4333.333008	4361.538574	4104.102539	4333.333008	4200.512695	4401.025391	4250.769043	4374.871582	4326.666504
1	4163.076660	4274.871582	4498.461426	4344.102539	4192.820313	4264.102539	4376.410156	4302.051270	4318.974121	4404.615234	4430.769043	4336.922852	4357.948730	4112.820313	4330.769043	4196.922852	4413.846191	4258.974121	4366.666504	4326.153809
2	4157.948730	4284.102539	4496.410156	4342.051270	4193.333008	4268.205078	4375.897461	4302.051270	4314.358887	4405.641113	4438.974121	4329.743652	4356.922852	4112.307617	4327.692383	4193.333008	4416.922852	4260.000000	4363.076660	4326.153809
3	4161.025391	4292.307617	4494.358887	4340.512695	4188.205078	4272.307617	4378.461426	4303.589844	4321.025391	4402.051270	4437.948730	4322.051270	4361.538574	4105.641113	4308.205078	4196.922852	4410.256348	4250.769043	4365.127930	4331.794922
4	4161.025391	4295.384277	4483.589844	4342.563965	4189.230469	4271.794922	4380.000000	4301.538574	4328.717773	4404.615234	4438.461426	4326.153809	4363.589844	4108.717773	4300.000000	4201.538574	4408.205078	4248.717773	4362.563965	4333.333008
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1276	4415.384277	4380.512695	4365.641113	4420.000000	4110.769043	4342.051270	4396.922852	4402.563965	4326.666504	4306.153809	4322.563965	4454.358887	4241.025391	4188.717773	4288.205078	4373.333008	4318.974121	4340.000000	4181.025391	4167.692383
1277	4413.333008	4378.974121	4369.743652	4425.127930	4107.179199	4346.153809	4393.333008	4404.615234	4334.871582	4298.974121	4327.692383	4459.487305	4237.436035	4144.102539	4284.615234	4371.794922	4315.384277	4346.153809	4187.179199	4176.922852
1278	4410.769043	4374.871582	4362.563965	4430.256348	4117.436035	4350.769043	4391.281738	4400.000000	4336.922852	4265.641113	4330.769043	4447.179199	4241.025391	4118.461426	4283.589844	4371.281738	4322.563965	4351.794922	4191.794922	4175.897461
1279	4409.743652	4388.717773	4361.025391	4434.871582	4104.102539	4349.230469	4388.717773	4398.974121	4332.820313	4231.281738	4318.974121	4429.743652	4249.743652	4121.538574	4288.205078	4371.794922	4324.102539	4357.948730	4193.333008	4178.974121
1280	4412.307617	4392.820313	4361.538574	4432.307617	4084.102539	4351.281738	4386.666504	4404.102539	4334.871582	4230.769043	4302.051270	4427.179199	4250.256348	4129.230469	4289.230469	4370.256348	4317.436035	4362.051270	4193.846191	4188.717773

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
0	4276.410156	4288.205078	4447.179199	4604.615234	4016.410156	4143.076660	4317.948730	4304.615234	4308.717773	4241.538574	4328.717773	4373.846191	4174.871582	3983.076904	4224.102539	4227.692383	4334.871582	4289.743652	4283.076660	4397.948730
1	4277.948730	4290.256348	4450.256348	4598.974121	4006.666504	4146.153809	4317.436035	4329.230469	4303.589844	4240.512695	4333.846191	4380.000000	4183.589844	3988.205078	4229.230469	4216.922852	4331.281738	4302.051270	4287.179199	4404.615234
2	4277.436035	4293.333008	4447.692383	4602.051270	3978.461426	4148.205078	4324.102539	4348.717773	4305.127930	4247.179199	4331.794922	4375.384277	4194.871582	3990.256348	4235.384277	4212.307617	4331.794922	4303.076660	4293.333008	4402.563965
3	4271.794922	4277.948730	4450.256348	4605.127930	3980.000000	4148.205078	4328.205078	4343.076660	4309.743652	4251.794922	4334.871582	4364.102539	4201.025391	3960.512695	4235.384277	4204.102539	4335.897461	4297.948730	4291.281738	4406.153809
4	4272.307617	4253.846191	4455.384277	4606.666504	4002.563965	4150.256348	4326.666504	4335.897461	4310.256348	4257.436035	4340.512695	4360.000000	4207.179199	3875.384521	4234.871582	4182.563965	4337.948730	4295.897461	4285.127930	4407.692383
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1276	4327.692383	4288.205078	4378.974121	4306.666504	4198.974121	4513.333008	4376.410156	4350.256348	4421.538574	4371.281738	4405.641113	3423.589600	4358.461426	4174.871582	4325.127930	4155.384277	4331.794922	4380.000000	4346.666504	4256.922852
1277	4332.820313	4285.641113	4376.922852	4276.922852	4201.025391	4512.820313	4381.025391	4347.692383	4431.281738	4371.794922	4407.179199	3432.307617	4359.487305	4180.000000	4320.000000	4158.974121	4324.615234	4381.538574	4344.615234	4254.871582
1278	4329.743652	4292.820313	4378.461426	4262.051270	4204.615234	4517.948730	4381.538574	4347.692383	4431.794922	4371.281738	4407.179199	3447.179443	4357.948730	4184.615234	4316.922852	4160.512695	4316.410156	4383.589844	4346.666504	4252.307617
1279	4328.205078	4290.769043	4380.000000	4270.256348	4204.615234	4517.948730	4373.846191	4352.820313	4424.615234	4376.922852	4408.205078	3454.358887	4356.922852	4185.641113	4316.922852	4161.538574	4309.743652	4390.256348	4344.615234	4253.333008
1280	4327.692383	4285.127930	4381.538574	4276.922852	4201.025391	4515.897461	4370.769043	4353.846191	4422.563965	4373.846191	4409.230469	3458.461426	4357.948730	4185.641113	4321.025391	4169.230469	4295.897461	4389.230469	4337.948730	4253.846191

Data Acquisition¶

Import Libraries, Function and Files¶

Cropping Data¶

Set Channel F3¶

Preprocessing¶

Filtering¶

Fast Fourier Transform¶

Feature Extraction¶

Power Spectral Density¶

Entropy¶

Statistical Feature¶

Normalization Feature¶

Labeling¶

Heatmap¶

Classification¶

Splitting Data¶

Training Model¶

Evaluation¶

Retraining¶

Confusion Matrix¶

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
0	5023.618992	5142.743717	5421.730846	5223.600829	5045.839066	5132.868127	5273.596435	5178.543017	5198.911859	5302.606122	5327.912171	5215.576473	5249.524542	4939.675905	5215.576473	5055.714654	5297.050664	5116.202926	5265.572079	5207.552705
1	5012.799247	5147.671489	5415.740224	5226.422186	5046.327547	5133.846425	5269.092716	5178.137613	5197.137578	5301.976821	5334.055679	5216.163103	5246.221518	4946.834730	5211.935923	5051.561614	5308.884168	5123.357958	5257.423247	5207.242617
2	5007.389540	5156.017483	5411.752854	5226.186545	5045.264855	5137.189538	5267.957433	5178.388202	5196.573743	5301.194290	5339.660164	5211.104228	5245.921780	4947.431589	5203.359496	5049.585429	5312.688784	5123.761949	5253.330393	5208.805935
3	5007.387169	5164.520165	5406.624495	5225.410130	5043.066030	5140.567317	5269.646514	5178.608955	5201.176272	5300.146246	5341.927771	5206.160960	5248.638546	4944.800626	5189.311202	5051.915531	5309.540930	5118.628049	5252.357247	5212.376059
4	5009.196317	5171.570941	5399.850042	5225.813831	5044.569644	5142.054579	5272.233154	5177.935176	5207.014241	5299.120947	5343.760408	5208.450017	5250.061777	4945.949868	5182.306683	5056.915486	5305.962560	5116.550684	5250.429817	5214.640234
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1276	5314.486135	5269.205452	5259.381385	5320.240093	4948.925188	5228.150793	5291.283431	5298.487843	5208.462976	5183.396906	5205.769292	5359.696282	5102.018486	5033.889980	5160.256536	5263.495406	5198.129912	5223.049791	5033.064318	5017.467018
1277	5311.744003	5269.821755	5255.943661	5326.069214	4947.702614	5231.185672	5288.262899	5299.125259	5215.226410	5165.222780	5207.942905	5361.177779	5102.580670	4994.544490	5157.886887	5262.233899	5197.545834	5230.796310	5039.120125	5023.963062
1278	5309.383424	5271.501896	5252.504810	5331.888301	4947.717003	5234.527944	5285.192416	5297.048106	5217.765808	5133.927719	5207.173294	5350.762721	5106.225698	4967.450722	5157.635510	5261.582932	5200.719093	5237.914184	5044.096814	5027.427831
1279	5308.821890	5279.281614	5249.743342	5335.290896	4936.825275	5235.810022	5282.403491	5296.792523	5216.943297	5104.427652	5196.171999	5336.635813	5112.251294	4962.936506	5160.150723	5261.183226	5201.500208	5244.505536	5046.646104	5032.189478
1280	5310.629891	5287.175076	5249.524542	5334.701784	4915.604011	5237.179469	5279.768384	5300.754303	5217.428293	5092.131033	5177.925940	5328.529248	5115.585849	4969.919747	5162.494893	5260.017208	5196.442961	5250.141620	5047.690885	5041.518349

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
0	5147.064433	5161.260738	5352.601141	5542.090312	4834.129821	4986.584949	5197.060038	5181.011914	5185.949708	5105.093183	5210.021601	5264.337924	5024.853147	4794.010096	5084.107264	5088.427981	5217.428293	5163.112557	5155.088201	5293.347611
1	5148.089586	5163.650667	5354.472827	5538.317615	4816.714627	4989.886655	5198.865616	5208.277835	5182.154873	5106.473677	5213.829642	5267.864980	5036.114258	4798.351880	5090.490744	5077.506380	5214.610414	5173.878939	5160.543050	5298.484384
2	5146.619641	5160.866706	5354.921359	5539.170933	4798.815721	4992.068594	5203.615789	5225.233237	5182.755322	5111.369175	5215.394051	5263.797560	5047.209138	4790.310033	5095.564855	5068.501447	5215.016784	5176.835925	5164.543993	5300.604674
3	5143.639789	5146.062225	5357.153873	5542.153657	4797.892190	4993.324624	5207.487819	5226.385609	5185.835268	5117.475422	5218.384800	5255.057294	5056.297065	4748.973255	5097.345095	5055.435471	5218.027234	5173.882389	5163.498098	5302.534541
4	5143.960741	5129.010091	5360.169202	5544.135024	4812.090146	4993.855777	5208.805961	5221.599125	5187.612733	5120.238981	5223.532262	5249.579247	5064.980501	4693.860335	5096.437203	5035.557628	5220.989372	5170.246848	5159.747514	5303.549756
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1276	5207.054987	5164.137259	5270.059400	5177.556473	5057.062921	5433.657457	5269.916844	5232.280583	5325.142818	5264.575809	5299.836932	4123.181459	5247.264477	5028.259352	5205.145302	5003.286911	5209.748971	5270.366066	5230.687288	5123.681362
1277	5212.045812	5161.522325	5269.257786	5152.961884	5056.994267	5433.534399	5271.537349	5233.614722	5330.331531	5261.879194	5303.759154	4133.225410	5246.276906	5030.991632	5200.275997	5005.130349	5204.497341	5273.774586	5230.417431	5121.056499
1278	5211.620138	5163.763548	5269.975941	5138.082675	5059.364676	5436.087035	5270.767761	5234.595889	5331.327631	5263.161752	5304.773370	4146.415666	5245.371422	5035.305113	5197.034383	5007.503198	5195.545274	5277.495573	5230.189655	5119.275095
1279	5209.791732	5162.893587	5271.746234	5139.669357	5059.326994	5436.952074	5265.742424	5237.732045	5327.035029	5265.166394	5305.715128	4156.406534	5244.690036	5037.376386	5197.317885	5011.100029	5184.734742	5281.534283	5227.464547	5119.194976
1280	5208.787447	5157.557099	5273.596435	5147.681511	5056.331732	5435.310074	5260.634285	5240.266031	5322.974377	5264.337924	5306.926252	4162.585708	5245.203825	5037.815297	5200.763091	5018.063533	5170.519249	5282.854359	5221.131932	5119.906565

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
0	6.636871e+06	6.738258e+06	6.747208e+06	6.769092e+06	6.322167e+06	6.449325e+06	6.629144e+06	6.637523e+06	6.695402e+06	6.673422e+06	6.756949e+06	6.676797e+06	6.688475e+06	6.458320e+06	6.532971e+06	6.684201e+06	6.691187e+06	6.671684e+06	6.755979e+06	6.694968e+06
1	2.912420e+04	3.499883e+04	6.864195e+04	4.239030e+04	1.373386e+04	8.598626e+04	2.257963e+04	4.135451e+04	4.471730e+04	1.376212e+04	2.552434e+04	3.251306e+04	3.361578e+04	8.103503e+03	3.641821e+04	3.776819e+04	6.910825e+04	2.694972e+04	5.443365e+04	3.423020e+04
2	4.938947e+04	3.742747e+04	1.424622e+04	4.769630e+04	8.348167e+03	6.410478e+04	5.159508e+04	3.958217e+04	9.266842e+03	3.724818e+04	1.463693e+04	1.281266e+04	9.203949e+03	4.205576e+04	3.454984e+04	3.557810e+03	2.028223e+04	1.339374e+04	2.673127e+04	1.447533e+04
3	1.914127e+04	8.933757e+04	3.624079e+04	1.475893e+04	2.970978e+03	1.261235e+04	3.755111e+04	3.833346e+04	2.294031e+04	3.431509e+04	1.888790e+04	2.004339e+04	2.422466e+04	1.578900e+04	1.002371e+04	3.423227e+04	4.776111e+04	1.201863e+04	1.847734e+04	3.889765e+04
4	2.020634e+04	1.054444e+04	2.440861e+04	1.780810e+04	1.780697e+04	3.294964e+04	1.975547e+04	2.886908e+04	2.866454e+03	3.025963e+04	1.220568e+04	1.977023e+03	3.248081e+03	1.964703e+04	1.923526e+04	2.547864e+04	7.855791e+03	1.195007e+04	1.332843e+04	2.802802e+04
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
635	1.410990e+02	7.444429e+01	8.776465e+01	5.604191e+01	7.083062e+01	5.264935e+01	1.019176e+00	6.237307e+01	9.501615e+00	1.064234e+02	7.864788e+01	5.621858e+01	6.766079e+01	1.973572e+01	2.694907e+01	1.006660e+02	4.849300e+01	7.055902e+01	1.111358e+02	8.038327e+01
636	1.410943e+02	7.444173e+01	8.776233e+01	5.603944e+01	7.082867e+01	5.264759e+01	1.018942e+00	6.237055e+01	9.500061e+00	1.064197e+02	7.864665e+01	5.621608e+01	6.765816e+01	1.973521e+01	2.694611e+01	1.006631e+02	4.849191e+01	7.055713e+01	1.111314e+02	8.038195e+01
637	1.410903e+02	7.444058e+01	8.775989e+01	5.603882e+01	7.082679e+01	5.264598e+01	1.019602e+00	6.236905e+01	9.499915e+00	1.064177e+02	7.864494e+01	5.621500e+01	6.765640e+01	1.973449e+01	2.694634e+01	1.006609e+02	4.849028e+01	7.055573e+01	1.111291e+02	8.037938e+01
638	1.410881e+02	7.443896e+01	8.775872e+01	5.603807e+01	7.082521e+01	5.264512e+01	1.019091e+00	6.236858e+01	9.499573e+00	1.064157e+02	7.864295e+01	5.621440e+01	6.765542e+01	1.973452e+01	2.694542e+01	1.006587e+02	4.848914e+01	7.055427e+01	1.111269e+02	8.037835e+01
639	1.410863e+02	7.443811e+01	8.775766e+01	5.603733e+01	7.082427e+01	5.264467e+01	1.019091e+00	6.236770e+01	9.499381e+00	1.064146e+02	7.864207e+01	5.621337e+01	6.765444e+01	1.973403e+01	2.694500e+01	1.006576e+02	4.848862e+01	7.055350e+01	1.111256e+02	8.037722e+01

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
0	6.685101e+06	6.673000e+06	6.703298e+06	6.750850e+06	6.354045e+06	6.685276e+06	6.619522e+06	6.641167e+06	6.749840e+06	6.677142e+06	6.751121e+06	6.610367e+06	6.712154e+06	6.519289e+06	6.574786e+06	6.586368e+06	6.643646e+06	6.693521e+06	6.699553e+06	6.762855e+06
1	4.866684e+04	6.130269e+04	2.165192e+04	2.840652e+04	7.884178e+04	5.328847e+04	1.057832e+05	5.400525e+04	6.731749e+04	4.656792e+04	6.256182e+04	1.891853e+04	3.393802e+04	3.293704e+04	4.195691e+04	6.917589e+04	2.236794e+04	3.564499e+04	3.531990e+04	2.959156e+04
2	3.233926e+04	3.075478e+04	8.207080e+03	1.164771e+03	2.875108e+04	3.707064e+04	2.123084e+04	2.854249e+04	4.979736e+04	2.398951e+04	2.097497e+04	1.023772e+05	4.096156e+04	8.374310e+04	2.159530e+04	3.820125e+04	1.145969e+04	1.458225e+04	2.482035e+04	2.416521e+04
3	2.777043e+04	9.784667e+03	4.282082e+04	2.053382e+04	9.203555e+04	4.415848e+04	1.778644e+04	3.887262e+04	2.871289e+04	2.181220e+04	2.966341e+04	5.471631e+04	4.428728e+04	3.808468e+03	3.062262e+04	2.751657e+04	1.502005e+04	1.661913e+04	1.736718e+04	2.930911e+04
4	2.917422e+04	4.670411e+03	2.539113e+04	1.548108e+04	2.888602e+04	4.337548e+04	4.103246e+04	2.345691e+04	3.757567e+04	3.179966e+04	1.433362e+04	3.266775e+04	5.501900e+04	3.436169e+04	1.795078e+04	3.081883e+04	1.478372e+04	1.028837e+04	4.377318e+03	4.494690e+04
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
635	3.110130e+01	2.834611e+00	3.824271e+01	1.966943e+02	1.067806e+02	2.247158e+02	3.076986e+01	3.730843e+01	6.639802e+01	7.875760e+01	5.015070e+01	5.480111e+02	1.131264e+02	1.234023e+02	6.103679e+01	3.612312e+01	2.845105e+01	6.318641e+01	3.239995e+01	8.475028e+01
636	3.109993e+01	2.832988e+00	3.824035e+01	1.966896e+02	1.067787e+02	2.247088e+02	3.076902e+01	3.730842e+01	6.639504e+01	7.875704e+01	5.014813e+01	5.479947e+02	1.131231e+02	1.233974e+02	6.103566e+01	3.612342e+01	2.844935e+01	6.318439e+01	3.240045e+01	8.474759e+01
637	3.109897e+01	2.833042e+00	3.823864e+01	1.966841e+02	1.067752e+02	2.247040e+02	3.076820e+01	3.730727e+01	6.639364e+01	7.875400e+01	5.014734e+01	5.479817e+02	1.131208e+02	1.233932e+02	6.103427e+01	3.612302e+01	2.844870e+01	6.318254e+01	3.239973e+01	8.474663e+01
638	3.109901e+01	2.832834e+00	3.823811e+01	1.966803e+02	1.067731e+02	2.246997e+02	3.076773e+01	3.730629e+01	6.639275e+01	7.875286e+01	5.014614e+01	5.479723e+02	1.131181e+02	1.233903e+02	6.103335e+01	3.612138e+01	2.844802e+01	6.318177e+01	3.239834e+01	8.474455e+01
639	3.109851e+01	2.832843e+00	3.823767e+01	1.966780e+02	1.067718e+02	2.246971e+02	3.076714e+01	3.730605e+01	6.639179e+01	7.875212e+01	5.014545e+01	5.479655e+02	1.131169e+02	1.233881e+02	6.103271e+01	3.612137e+01	2.844777e+01	6.318080e+01	3.239801e+01	8.474352e+01

	delta	theta	alpha	beta
0	0.034556	0.076501	0.067483	0.230142
1	0.046539	0.049498	0.048339	0.186967
2	0.077010	0.057129	0.073475	0.220910
3	0.052009	0.065631	0.062209	0.195104
4	0.062595	0.048581	0.043320	0.323552
5	0.041028	0.056193	0.061224	0.309484
6	0.051230	0.040134	0.049657	0.269233
7	0.049822	0.050272	0.075331	0.309986
8	0.066299	0.048886	0.050576	0.287538
9	0.042296	0.093588	0.070265	0.256152
10	0.017390	0.070242	0.071162	0.357277
11	0.036992	0.026919	0.102285	0.278286
12	0.050425	0.068950	0.045441	0.287481
13	0.045168	0.032941	0.056652	0.267882
14	0.037876	0.050107	0.081672	0.350718
15	0.052029	0.051347	0.048946	0.247716
16	0.093829	0.028002	0.047603	0.331691
17	0.060638	0.070508	0.048212	0.220416
18	0.050627	0.059317	0.062637	0.260917
19	0.040528	0.053551	0.071544	0.298648

	delta	theta	alpha	beta
0	0.045657	0.052829	0.113011	0.278737
1	0.073855	0.095474	0.077504	0.259815
2	0.047561	0.064578	0.069451	0.330307
3	0.059468	0.047405	0.051626	0.285080
4	0.032684	0.038351	0.048928	0.245009
5	0.043561	0.072305	0.068141	0.230281
6	0.082703	0.070086	0.052686	0.259997
7	0.034529	0.059071	0.022520	0.235574
8	0.042204	0.025418	0.066486	0.274210
9	0.064815	0.039218	0.099146	0.279214
10	0.028852	0.035649	0.078844	0.292869
11	0.034370	0.066801	0.047832	0.247040
12	0.044788	0.036548	0.066373	0.329328
13	0.090509	0.075467	0.094019	0.186177
14	0.039060	0.040785	0.084241	0.242107
15	0.027008	0.094852	0.063980	0.262093
16	0.051717	0.107500	0.058318	0.366065
17	0.059056	0.105731	0.065285	0.316432
18	0.045102	0.073012	0.060312	0.284514
19	0.046701	0.062353	0.070722	0.248787

	entropy
0	1.143313
1	1.163421
2	1.221020
3	1.217001
4	0.980342
5	1.007474
6	1.019207
7	1.044032
8	1.054774
9	1.155655
10	0.913472
11	1.007992
12	1.050234
13	0.997259
14	0.972643
15	1.082628
16	0.971616
17	1.175462
18	1.108041
19	1.033990

	entropy
0	1.120447
1	1.224905
2	1.035635
3	1.042828
4	0.989755
5	1.164807
6	1.163968
7	0.971924
8	0.970355
9	1.115388
10	0.960992
11	1.062028
12	0.949151
13	1.317020
14	1.090651
15	1.089606
16	1.049094
17	1.128456
18	1.082873
19	1.135034

	mean
0	0.310399
1	0.298953
2	0.329909
3	0.318391
4	0.291678
5	0.295081
6	0.285892
7	0.305889
8	0.301615
9	0.323591
10	0.285909
11	0.290495
12	0.300506
13	0.279980
14	0.298603
15	0.296533
16	0.294548
17	0.315047
18	0.308308
19	0.299652

	mean
0	0.322136
1	0.346310
2	0.309506
3	0.297282
4	0.270946
5	0.315819
6	0.325888
7	0.264723
8	0.275735
9	0.319556
10	0.279441
11	0.291614
12	0.285238
13	0.352638
14	0.299369
15	0.307508
16	0.326539
17	0.334992
18	0.309163
19	0.312720

	var
0	0.222498
1	0.237148
2	0.252521
3	0.255794
4	0.162133
5	0.171003
6	0.177325
7	0.182235
8	0.187428
9	0.223270
10	0.140915
11	0.171028
12	0.185870
13	0.170169
14	0.158459
15	0.200381
16	0.158029
17	0.236248
18	0.207642
19	0.179749

	var
0	0.207971
1	0.247204
2	0.178482
3	0.183832
4	0.169380
5	0.230716
6	0.226483
7	0.163732
8	0.160870
9	0.206760
10	0.156790
11	0.192917
12	0.152572
13	0.292540
14	0.202578
15	0.199237
16	0.179774
17	0.207843
18	0.196664
19	0.218075

	std
0	0.471697
1	0.486979
2	0.502515
3	0.505761
4	0.402657
5	0.413525
6	0.421099
7	0.426890
8	0.432930
9	0.472515
10	0.375386
11	0.413555
12	0.431126
13	0.412515
14	0.398068
15	0.447640
16	0.397529
17	0.486054
18	0.455678
19	0.423969

	std
0	0.456038
1	0.497196
2	0.422471
3	0.428757
4	0.411558
5	0.480329
6	0.475902
7	0.404638
8	0.401086
9	0.454708
10	0.395967
11	0.439224
12	0.390605
13	0.540870
14	0.450087
15	0.446360
16	0.423997
17	0.455898
18	0.443468
19	0.466985

	delta	theta	alpha	beta	entropy	mean	std	var	min	max
0	0.224576	0.743703	0.409782	0.253505	0.747335	0.609249	0.738726	0.710168	0.431980	0.747335
1	0.381333	0.338677	0.085124	0.000000	0.812714	0.379995	0.855938	0.837693	0.733507	0.812714
2	0.779967	0.453134	0.511401	0.199298	1.000000	1.000000	0.975105	0.971513	1.000000	1.000000
3	0.452898	0.580668	0.320354	0.047774	0.986933	0.769318	1.000000	1.000000	0.871165	0.986933
4	0.591392	0.324916	0.000000	0.801978	0.217430	0.234292	0.209173	0.184698	0.652508	0.217430
5	0.309246	0.439091	0.303634	0.719378	0.305651	0.302442	0.292530	0.261909	0.594845	0.305651
6	0.442711	0.198220	0.107479	0.483037	0.343801	0.118414	0.350628	0.316940	0.572332	0.343801
7	0.424288	0.350285	0.542886	0.722320	0.424520	0.518912	0.395042	0.359682	0.816133	0.424520
8	0.639851	0.329494	0.123062	0.590514	0.459445	0.433308	0.441368	0.404887	0.792570	0.459445
9	0.325833	1.000000	0.456968	0.406228	0.787465	0.873475	0.744997	0.716888	0.626752	0.787465
10	0.000000	0.649820	0.472184	1.000000	0.000000	0.118739	0.000000	0.000000	0.000000	0.000000
11	0.256445	0.000000	1.000000	0.536192	0.307335	0.210594	0.292761	0.262125	0.239779	0.307335
12	0.432175	0.630441	0.035964	0.590179	0.444685	0.411104	0.427536	0.391323	0.705872	0.444685
13	0.363396	0.090332	0.226108	0.475100	0.272434	0.000000	0.284786	0.254649	0.391329	0.272434
14	0.268006	0.347817	0.650422	0.961489	0.192397	0.372999	0.173976	0.152713	0.515518	0.192397
15	0.453162	0.366415	0.095409	0.356693	0.550014	0.331533	0.554199	0.517644	0.794077	0.550014
16	1.000000	0.016246	0.072636	0.849767	0.189056	0.291775	0.169839	0.148977	0.267036	0.189056
17	0.565782	0.653814	0.082959	0.196399	0.851867	0.702344	0.848844	0.829860	0.775603	0.851867
18	0.434821	0.485950	0.327598	0.434209	0.632647	0.567366	0.615854	0.580850	0.836392	0.632647
19	0.302703	0.399471	0.478666	0.655747	0.391868	0.394007	0.372636	0.338047	0.582260	0.391868

	delta	theta	alpha	beta	entropy	mean	std	var	min	max
0	0.293677	0.333942	1.000000	0.514545	0.465643	0.653049	0.435453	0.395795	0.436979	0.465643
1	0.737736	0.853481	0.607624	0.409353	0.749597	0.928023	0.709356	0.676097	0.969551	0.749597
2	0.323664	0.477075	0.518627	0.801219	0.235095	0.509389	0.212067	0.185111	0.472942	0.235095
3	0.511171	0.267866	0.321643	0.549806	0.254649	0.370337	0.253896	0.223337	0.470007	0.254649
4	0.089393	0.157553	0.291835	0.327050	0.110378	0.070775	0.139442	0.120084	0.191975	0.110378
5	0.260674	0.571222	0.504146	0.245175	0.586230	0.581194	0.597107	0.558299	0.397396	0.586230
6	0.877083	0.544180	0.333359	0.410365	0.583949	0.695724	0.567646	0.528055	0.569740	0.583949
7	0.118434	0.409985	0.000000	0.274598	0.061906	0.000000	0.093389	0.079730	0.000000	0.061906
8	0.239308	0.000000	0.485866	0.489376	0.057639	0.125249	0.069747	0.059281	0.054743	0.057639
9	0.595386	0.168121	0.846783	0.517195	0.451891	0.623705	0.426601	0.387140	0.315376	0.451891
10	0.029043	0.124640	0.622428	0.593100	0.032187	0.167408	0.035684	0.030133	0.119596	0.032187
11	0.115935	0.504161	0.279714	0.338339	0.306840	0.305871	0.323552	0.288245	0.223807	0.306840
12	0.279995	0.135596	0.484609	0.795781	0.000000	0.233342	0.000000	0.000000	0.264952	0.000000
13	1.000000	0.609738	0.790123	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
14	0.189791	0.187214	0.682070	0.310915	0.384647	0.394078	0.395845	0.357265	0.312385	0.384647
15	0.000000	0.845908	0.458168	0.422017	0.381808	0.486657	0.371044	0.333397	0.084763	0.381808
16	0.389114	1.000000	0.395594	1.000000	0.271680	0.703127	0.222224	0.194341	0.551438	0.271680
17	0.504688	0.978445	0.472584	0.724089	0.487417	0.799280	0.434522	0.394884	0.690050	0.487417
18	0.284948	0.579833	0.417633	0.546657	0.363503	0.505480	0.351800	0.315013	0.426510	0.363503
19	0.310123	0.449978	0.532676	0.348051	0.505296	0.545938	0.508301	0.467983	0.456702	0.505296

	Train Accuracy	Test Accuracy
K = 1	1.0	0.666667
K = 2	0.785714	0.833333
K = 3	0.785714	0.75
K = 4	0.75	0.75
K = 5	0.785714	0.833333
K = 6	0.821429	0.75
K = 7	0.785714	0.833333
K = 8	0.785714	0.75
K = 9	0.785714	0.833333
K = 10	0.821429	0.833333